Characterizing fire severity patterns in three wildland fire use incidents in the
southern Sierra Nevada
Report prepared by Nicole Vaillant
USDA Forest Service
Adaptive Management Services Enterprise Team
July 1, 2009
for the Sequoia National Forest
Characterizing fire severity patterns in three wildland fire use incidents in the
southern Sierra Nevada
Summary
Wildland fire use (WFU) is a tool that has been utilized by managers in the Forest
Service since the 1970s to reintroduce fire as a natural ecosystem process. Today it is
also applied to meet additional resource objectives including restoration and
maintenance of ecosystems and fire hazard reduction through lessening the extent and
severity of future fires. Few studies have characterized the spatial pattern of fire severity
in either wildland or WFU fires. The objectives of this report are to: 1) use remotely
sensed data and geospatial analysis to understand the influence of weather,
topography, and fuels on fire severity, and 2) characterize fire severity patch dynamics
for the Albanita-Hooker, Crag, and Clover WFU Fires in the southern Sierra Nevada. A
regression tree analysis was completed with elevation, slope, aspect, time since last
fire, burn frequency, wind speed, temperature, relative humidity, energy release
component, and vegetation type as explanatory values to describe change in canopy
cover derived from remotely sensed data. Change in canopy cover was most described
by relative humidity, slope, and vegetation type for the Albanita-Hooker Fire; elevation,
temperature, and energy release component for the Crag Fire; and relative humidity,
temperature, and energy release component for the Clover Fire. The majority of the
Albanita-Hooker and Crag Fires resulted in unchanged to low fire severity (70 % and 89
%, respectively). Mean (area-weighted) patch size for unburned to low severity was
approximately 10 and 50 times the size of moderate and high severity patches for the
Albanita-Hooker Fire, and about 100 times the size of both moderate and high severity
patches in the Crag Fire. In contrast, a bimodal distribution of fire severity was seen in
the Clover Fire, with 28 % unchanged to low severity and 62 % high severity, with the
largest mean (area-weighted) patches occurring in the high severity category (6,889
ac). It is intended that the findings from this report will help managers understand which
factors have the most influence on fire severity, and use this information to determine if
WFU incidents will adequately return landscapes to a more historical fire regime.
Key Findings
The Albanita-Hooker and Crag Fires primarily resulted in unchanged to low fire
severity; the majority of the Clover Fire resulted in high severity
Based on regression tree analysis topographic features and weather variables
were the most influential factors determining canopy cover change for all three
fires
The Clover Fire reburned seven fires, with the most recent fires appearing to
limit fire growth
Some vegetation types such as pine experiencing higher severity than what
would have historically occurred
Page 2
Background
It is accepted that over a century of fire exclusion and aggressive land management
practices has altered the fire regime in the Sierra Nevada from one of frequent, low
intensity fires to less frequent high intensity fires (McKelvey and Busse 1996). Recent
work using remotely sensed data has shown the frequency, extent, and severity of
wildfires to drastically increase in the Sierra Nevada from the 1970s until present (Miller
et al. 2009a). Fire severity refers to the loss of organic matter above or below ground
and describes the impact of fire on an ecosystem (Keeley 2009) and can be assessed
remotely with the use of satellite imagery (Miller and Thode 2007). Managers can alter
potential fire behavior and severity by manipulating surface and canopy fuels to reduce
the overall fuel load and by breaking the vertical and horizontal continuity of fuels
(Graham et al. 2004). Treatments are usually implemented with prescribed fire, manual
or mechanical thinning of smaller diameter trees, or mechanical methods such as
mastication (Graham et al. 2004). Unfortunately, these treatments are not always
feasible or permitted in remote areas or in areas designated as wilderness. These areas
present an opportunity for managers reintroducing fire to the ecosystem through the use
of wildland fire (Miller 2003). Wildland fire use (WFU) is defined as “management of
either wildfire or prescribed fire to meet resource objectives specified in Land/Resource
Management Plans” (USDA/USDI 2009).
Although the terminology and the definition are new, the concept is not. Native
Americans used fire to manipulate the landscape to enhance food production and
materials for making baskets (Anderson 2006, van Wagtendonk 2007). With EuroAmerican settlement, the perception of fire in the western United States was one of fear
and destruction and fire suppression became the policy (Pyne 1982). It was not until the
late 1970s that the Forest Service changed their policy of complete suppression to fire
management where suppression, WFU, and prescribed fire were all acceptable.
Wildland fire use was initiated to restore fire as a natural ecosystem process, with much
recognition of the benefits and needs associated with reintroducing fire, but with limited
explicit objectives; today WFU is used to meet an increasing number of more definitive
resource objectives associated with the maintenance of natural fire regimes, including
healthy ecosystem function, and managing for natural extent and severity of future fires
through fuel reduction (Zimmerman and Lasko 2006). Wildland fire use is viewed as a
cost effective tool to reduce surface and canopy fuel accumulations and continuity at the
landscape-level facilitating the creation of resilient forest (Black 2004). The goal of WFU
is that, in time, fire will play a more natural role creating a mosaic pattern of stands at
various stages of post-fire development over the landscape, gradually creating stand
structures which were prevalent before fire suppression became the policy (Wells
2009).
Unfortunately there are limitations to applying WFU. Many of these limitations are
political (Doane et al. 2006). In order to use WFU on any particular fire, both a Land and
Resource Management Plan and Fire Management Plan authorizing the use of WFU
must be in place, and then a recommendation to manage the fire as such must be made
(USDA/USDI 2005). Other limitations include public perception and acceptance,
fragmented landscapes with multiple ownership, smoke and air quality, and proximity
Page 3
and threat to valuable resources including property and natural resources. One of the
primary concerns of land managers is whether or not implementing a WFU fire will
result in the desired mosaic puzzle of forest stand structures (Miller 2007).
Objectives
To date few studies have characterized the spatial pattern of fire severity in either
wildland fires or WFU fires (Odion et al. 2004, Collins and Stephens 2007, Collins et al.
2007, Collins et al. 2009). The objectives of this report are to: 1) use remotely sensed
data and geospatial analysis to understand the influence of weather, topography, and
fuels on fire severity, and 2) characterize fire severity patch dynamics for three WFU
fires in the southern Sierra Nevada.
The Albanita-Hooker Fire (2003), Crag Fire (2005), and Clover Fire (2008) form a
continuous area incorporating a diverse landscape consisting of coniferous forests,
mountain chaparral, high elevation meadows, and desert brush. The fires occurred at
different times during the fire season and lasted from a few days to over a month.
Currently WFU and fire suppression both fall under the umbrella of appropriate
management response (AMR) where multiple management tactics and objectives can
be utilized on a single incident. It is intended that the findings from this report will help
managers understand which factors have the most influence on fire severity, and use
this information to determine if WFU incidents will adequately return landscapes to a
more historical fire regime.
Methods
Study area
This study incorporates the area burned by the Albanita-Hooker, Crag, and Clover Fires
(Table 1, Figure 1). The fires burned on both the Sequoia and Inyo National Forests.
The Albanita-Hooker Fire started the afternoon of September 3, 2003 as two separate
lightning caused fires: the Albanita and Hooker Fires. The fires were both managed as
WFU fires. Around October 6, the two fires burned together and became the AlbanitaHooker Fire. The fire was contained at 4,707 ac on October 30. The Crag Fire started
on July 24, 2005 at 15:34 from a lightning strike and was also managed as a WFU fire.
The Crag Fire was contained at 1,510 ac on September 19. The Clover Fire started on
May 28, 2008 from a lightning strike and was initially managed as a WFU fire. After a
period of significant fire growth where the community of Kennedy Meadows was
threatened, the fire was managed as a WFU fire in the western portion, and a
suppression fire in the eastern portion under AMR. The Clover Fire was contained on
June 30 at 15,046 ac.
Page 4
Table 1: Dates, size, and designation for the Albanita-Hooker, Crag, and Clover Fires.
2
Start date &
Containment
Fire size
Fire
1
Year
time
date
(ac)
designation
Albanita-Hooker Fire 2003
9/03 1500
10/30
4707
WFU
Crag Fire
2005
7/24 1534
9/19
1510
WFU
Clover Fire
2008
5/28 1400
6/30
15046
AMR
Start dates and time from ICS-209 forms (Available at: http://fam.nwcg.gov)
2
Fire size is based on the remotely sensed data not the official estimated fire size
WFU – wildland fire use; AMR - appropriate management response
1
The majority of the study area burned on the Kern Plateau subsection of the Sierra
Nevada bioregion (Cleland et al. 2007). The Kern Plateau subsection is a high plateau
west of the Sierra Nevada crest, with elevations ranging from about 3,000 to over
11,000 ft. It has a temperate to cold and semi-arid to subhumid climate and averages 10
to 30 in of precipitation per year with the majority falling as snow in the higher
elevations. Vegetation zones include eastside and upper montane zones and are
composed primarily of Jeffrey pine (Pinus jeffreyii), red fir (Abies magnifica), and
lodgepole pine (Pinus contorta) with an understory shrub layer containing manzanita
(Arctostaphylos sp.), mountain mahogany (Cercocarpus sp.), and ceanothus
Page 5
(Ceanoutus sp.). There are also large wet meadows bordered by areas of sagebrush
(Aremesia sp.) throughout the Kern Plateau.
A portion of the Clover Fire burned off of the Kern Plateau to the east into the Eastern
Slopes subsection of the Sierra Nevada bioregion (Cleland et al. 2007). This subsection
includes the very steep eastern slope of the Sierra Nevada. The Eastern Slopes
subsection average 8 to 50 in of precipitation per year with the majority falling as snow
in the higher elevations. Higher elevations singleleaf pinyon (Pinus monophylla), and
juniper (Juniperus sp.) trees are common in addition to those found at higher elevations
of the Kern Plateau. At mid to lower elevations the vegetation is dominated by shrubs,
including: creosotebush (Larrea sp.), saltbush (Atriplex sp.), blackbush (Coleogyne sp.),
bursages (Ambrosia sp.), and sagebrush.
Spatial data
Post-fire estimates of fire effects were based on the relative version of the delta
Normalized Burn Ratio (RdNBR) obtained from the US Forest Service Remote Sensing
Applications Center. Images were obtained one year before fire, and immediately postfire (Clover Fire), and one year post-fire (Albanita-Hooker and Crag Fires). Immediate
post fire data was used for the Clover Fire because this was the only available data at
the time of analysis. Percent canopy cover change (0 to 100 %) and fire severity
categories (unchanged to low <25 %, moderate 25-75 %, high >75 % change in canopy
cover) derived from RdNBR were used for this study (Figure 2).
Canopy cover change is a commonly accepted measure of fire severity. Furthermore,
canopy closure is important for characterizing habitat conditions for species such as the
California spotted owl (Strix occidentalis), northern goshawk (Accipiter gentilis), and
Page 6
fisher (Martes pennanti) which are potentially present in the study area. The RdNBR
data and its derivatives are all 30 m pixel ArcGIS (vers. 9.2, ESRI, Redlands, CA) GRID
data.
Landfire National products provide continuous 30 m pixel ArcGIS GRID data for fire
behavior, fire regime, vegetation, and fire effects for the entire United States (data
available at: www.landfire.gov). Elevation, slope, aspect, and existing vegetation data
were downloaded and utilized for this project. Existing vegetation data was simplified
into nine categories based on similar vegetation type or burning characteristics (Table
2).
Table 2: Area and percent of landscape burned in the Albanita-Hooker, Crag,
and Clover Fires by dominant vegetation group.
AlbanitaHooker Fire
Crag Fire
Clover Fire
Area (ac)
2455
%
52
712
%
47
4140
%
28
1067
23
187
12
783
5
3
<1
<1
<1
1042
7
Woodland
<1
<1
0
0
450
3
Riparian
Pine
Fir
Pinyon-juniper
Area (ac)
Area (ac)
181
4
77
5
179
1
Sage
29
1
23
2
2307
15
Shrub
790
17
354
23
4029
27
Grass
19
<1
7
<1
56
<1
Sparsely vegetated
141
3
145
10
1654
11
21
<1
4
<1
406
3
Non-burnable
Totals
4707
1510
15046
For each fire, fire progression maps were obtained from either the fire’s Incident
Management Team or the Sequoia National Forest (Figure 3). Progression maps
provide daily or multiple day fire perimeter growth which is used to relate weather
characteristics to burn periods. The progression maps were converted into 30 m pixel
ArcGIS GRIDS in order to assign weather metrics (see below for description) to each
point in the burned area.
Page 7
A fire atlas (Figure 4) was used to determine the burn frequency, which is the number of
times a pixel had burned prior to the fire event (0, 1, or 2), and the time since last fire.
The fire atlas includes all fires greater than 100 ac and many smaller fires that have
occurred in California from around 1900 through 2008 (data available at:
www.fs.fed.us/r5/rsl/clearninghouse). The earliest fire on record for the study area
occurred in 1944. For areas where no previous fire was recorded, a default value of 100
yrs was assigned. As with the fire progression maps, burn frequency and time since
burn maps were converted into 30 m pixel ArcGIS GRIDS for consistency among the
data.
Page 8
Weather data
Weather data was collected from the Blackrock remote automated weather station
(RAWS) which is located at 36° 5' 37" latitude, -118° 15' 40" longitude (Figure 1). Daily
maximum temperature, minimum relative humidity, windspeed, and energy release
component were calculated in Fire Family Plus (RMRS/SEM 2002) for the individual fire
dates associated with fire progression (Table 3, Figure 5). Calculated weather variables
were then applied to the fire progression maps to assign daily (or averages of values if
the progression was for multiple days) values for the statistical analysis.
Table 3: Summary statistics for weather and topographical features for the Albanita-Hooker, Crag, and
Clover Fires.
Albanita-Hooker Fire
Crag Fire
Clover Fire
Mean
Range
Mean
Range
Mean
Range
Maximum temperature (ºF)
80
60-83
75
61-83
78
61-80
Minimum relative humidity (%)
10
2-22
14
4-32
12
5-20
Wind gust speed (mph)
7.9
0-14.0
9.4
5.0-17.6
10.3
8.0-13.0
2
Energy release component (BTU/ft )
38
12-50
47
24-52
44
23-48
Slope (%)
13
0-41
10
0-48
22
0-55
Elevation (ft)
8527
7779-9406
8520 8015-9157
7717 4521-9469
Data analysis
To characterize the spatial patterns of fire severity FRAGSTATS (vers. 3, McGarigal et
al. 2002) was used in conjunction with ArcGIS. FRAGSTATS is a spatial pattern
analysis program and was used to calculate the number of patches, mean patch size,
and area-weighted mean patch size for the three fire severity categories for each fire.
An area-weighted mean puts more emphasis on the larger patches and less on the
smaller patches. A high proportion of patches in the burned area were represented by a
Page 9
single to a very few pixels in size which is why an area-weighted mean was used in
conjunction with the arithmetic mean.
A regression tree analysis was completed using R (ver. 2.8.1, R Core Development
Team 2008) to explain the variation in fire severity. Regression tree analyses are a form
of exploratory analysis where multiple explanatory or predictor variables are used to
characterize a singe response variable (De’ath and Fabricus 2000). With regression
tree analysis the response variable is continuous; however, the explanatory variables
can be either continuous or categorical. Regression tree analyses are simplistic yet
robust and are often used for ecological data analysis because they do not require the
data to be normally distributed. For this analysis the response variable was change in
canopy cover and the explanatory values were: elevation, slope, aspect, time since last
fire, burn frequency, wind gust speed, maximum temperature, minimum relative
humidity, energy release component, and vegetation type. The “sample” tool in ArcGIS
was used to extract values for the response and explanatory variables for each pixel in
all three fires for use in the regression tree analysis. Each tree was “pruned” with a
complexity parameter of 0.01 (Finney et al. 2005). The complexity parameter insures a
split must decrease the overall lack of fit by that amount.
Results
Spatial pattern of fire severity
Frequency distributions of percent change in canopy cover for both the Albanita-Hooker
and Crag Fires were skewed to the left with the majority of the burned areas having
unchanged to low fire severity; the Clover Fire had a bimodal distribution with the
majority of the fire area resulting in high severity (Figure 5). The distribution is further
exemplified by the fire severity maps (Figure 2) and the patch dynamic outputs (Table
4). Mean (area-weighted) patch size was largest for the unchanged to low severity class
for the Albanita-Hooker and Crag Fires and high severity for the Clover Fire, 2,688 ac,
1,315 ac, and 6,889 ac, respectively (Table 4).
Table 4
Patch dynamics of the Albanita-Hooker, Crag, and Cover Fires.
Number
Mean
Fire severity class
of
patch size
patches
(ac)
Unchanged to low
30
111
Albanita-Hooker
Moderate
64
15
Fire
High
47
10
Unchanged to low
1
1315
Crag Fire
Moderate
33
5
High
16
5
Unchanged to low
342
12
Clover Fire
Moderate
1457
<1
High
224
42
Page
10
Area-weighted
mean patch size
(ac)
2688
277
52
1315
12
15
3111
7
6889
Regression tree analysis
The resulting pruned regression trees are shown in Figures 6, 7, and 8. The regression
tree illustrates predictor variable influence decreasing from top to bottom (variables
shown to have the greatest influence on canopy cover change are located at the top of
the regression tree). The predictor variable and value (or categories) associated with
each split are shown to the left and right of each node. For the terminal nodes the
number of pixels and the mean change in canopy cover (%) are listed. The tree for the
Clover Fire was the most simplistic (5 splits) and the tree for the Crag Fire was the most
complex (15 splits). Although burn frequency and wind speed were also explanatory
values used in the regression tree analysis, neither was used in tree construction
because these values were not found to significantly describe canopy cover change.
The explanatory values used to describe canopy cover change for the Albanita-Hooker
Fire were: minimum relative humidity, maximum temperature, energy release
component, slope, elevation, aspect, and vegetation type (Figure 6). Minimum relative
humidity was the most influential factor for determining canopy cover change for the
Albanita-Hooker Fire. Lower relative humidity values (<3.5%) are associated with larger
changes in canopy cover. After relative humidity, vegetation type and slope were used
to explain the change in canopy cover due to fire. Pine, pinyon-juniper, shrubs, and
woodland areas contributed to larger changes in canopy cover than fir, grass, riparian,
sparsely vegetated, sage, and non-burnable types. Steeper slopes contributed to larger
changes in canopy cover than shallower slopes. The third level of importance for
explaining the change in canopy cover for this regression tree were vegetation type,
aspect, and elevation. South, southeast, and southwest aspects, higher elevations, and
pine, pinyon-juniper, shrubs, sparsely vegetated areas, and woodlands all contributed to
larger changes in canopy cover. Additional splits are attributed to vegetation type,
energy release component, temperature, and slope. The subsequent split for vegetation
type is similar in effect to the prior split; however, sparsely vegetated areas are now
associated with larger changes in canopy cover. Relative humidity (>3.5 %), shallow
Page
11
slopes (<14.5 %), and fir, grass, non-burnable, riparian, and sage vegetation types
explain the lowest change in canopy cover (3 %). Low relative humidity (<3.5 %), pine,
pinyon-juniper, shrubs, and woodlands above 2556 m in elevation are associated with
the highest change in canopy cover (66 %).
For the Crag Fire, elevation was the most significant factor explaining change in canopy
cover where the smallest change in canopy cover values were associated with
elevations less than 8,360 ft. After elevation, maximum temperature was the next most
important factor. Here higher temperatures resulted in higher severity. The third level of
explanation is from energy release component, where lower values were associated
with higher severity. Aspect and time since last fire are the next most important factors
in explaining fire severity. South and southeast aspects and greater than 51 years since
the last fire are associated with higher severity. There is no set trend for splits lower in
the tree, factors used to explain the variability are: elevation, relative humidity, slope,
Page
12
and energy release component. For specifics on additional splits, refer to Figure 7. High
elevation (8,500 ft), low energy release component (<22.5 BTU/ft2), and long fire free
intervals (>51 years) all contributed to the highest fire severity (81 % canopy cover
change) for the Crag Fire. The smallest change in canopy cover (0 %) was attributed to
high energy release component (>22.5 BTU/ft2), south and south east aspects, high
relative humidity (>23 %), shallow slopes (<13 %), and higher elevation (8,071 ft).
Weather factors and fire history were the only explanatory values used in constructing
the tree for the Clover Fire (Figure 8). Relative humidity was the most influential factor
for determining canopy cover change for the Clover Fire. Temperature and energy
release component were the next most influential explanatory values. Higher
temperatures and energy release component result in higher canopy cover change. The
final level is defined by energy release component and time since last fire which further
Page
13
differentiated change in canopy cover after temperature. Lower values of energy
release component and longer fire free intervals are associated with higher fire severity.
The highest and lowest values of fire severity were associated with relative humidity
>12.5 %, and an initial split to an energy release component >28 BTU/ft2. The
subsequent split to greater than or less than 45.5 BTU/ft2 defined the greatest (low
energy release component) and smallest (high energy release component) change to
canopy cover.
Discussion
One of the primary goals of WFU is to create a patchwork of fire resilient stands
returning forests to a historical fire regime (Wells 2009). A fire regime is comprised of
five factors: seasonality, frequency, size, intensity, and severity. The historical fire
regime in the Sierra Nevada is dependant on the ecological zone and the dominant
vegetation present. The fires studied in this report exist primarily in eastside forest and
woodland and upper montane forest (van Wagtendonk and Fites-Kaufman 2006). All
three ecological zones are characterized by summer and fall fires. The eastside forest
and woodland ecological zone can be divided into three major vegetation types: Jeffrey
pine, white fir/mixed conifer, and chaparral. Jeffrey pine forests are characterized by
frequent small to medium sized low severity fires, white fir/mixed conifer forests and
chaparral experience moderately frequent medium sized fires with mixed severity for the
forests and high for chaparral (van Wagtendonk and Fites-Kaufman 2006). Upper
montane forests are divided into red fir and Jeffrey pine, western white pine, and
mountain juniper vegetation groups. Red fir typically burns with medium frequency and
fire size with mixed severity, and the Jeffrey pine composite has moderate frequency,
small extent and low severity (van Wagtendonk and Fites-Kaufman 2006). Although the
frequency of fires vary in the different ecological zones, the extent is typically small to
medium and the severity is low to mixed in forested systems and high in shrublands.
Page
14
The Albanita-Hooker, Crag, and Clover Fires burned under different fire conditions. The
later season Albanita-Hooker and Crag Fires were smaller in extent and primarily
burned with unchanged to low severity with small patches of moderate to high severity.
The moderate spatial extent and the mixed severity witnessed in these fires are similar
to the historical fire regime. Similar findings were observed for WFU fires burning in the
Illilouette basin of Yosemite National Park (Collins and Stephens 2007, Collins et al.
2007, Collins et al. 2009). These fires did not differ in extent, frequency or severity from
the historical fire regime (Collins et al. 2009). The difference in fire regime for the
Albanita-Hooker and Crag Fires as compared to the fires in the Illilouette basin is the
frequency in which fires historically occurred. The burned area for these two fires has
been fire free for at least the past 100 years based on the fire atlas. The early season
Clover Fire resulted in a bimodal distribution with the majority of the fire burning large
patches under high severity. The rather large extent and large patches of high severity
are likely outside of the historical range. Although dominant vegetation type was not
used to describe fire severity in the regression analysis, the Clover Fire had the highest
proportion of shrub dominated vegetation of the three fires (42 % shrub and sage
categories combined) which could contribute to the large high severity patches. In
addition, the Clover Fire experienced burning periods of much larger growth than the
Albanita-Hooker and Crag Fires. In particular the June 23rd burning period resulted in
3,678 ha (9,089 ac) of growth (Figure 3) where fire behavior was plume dominated
(personal observation) likely contributing to higher fire severity over a large area than
was recorded for either the Albanita-Hooker or Crag Fires. Finally, the immediate postfire imagery was used for the Clover Fire (because this is the only available data at this
point), whereas, one year post-fire imagery was used for both the Albanita-Hooker and
Crag Fires. The immediate post-fire imagery has the potential to overestimate fire
severity; when the data becomes available it would be advantageous to compare the
data sets and rerun the analysis for consistency among the three fires.
Regression tree analyses were used to describe the influences of weather, topography,
fuels and fire history on fire severity which was characterized by percent change in
canopy cover. Weather and topographic features were the dominant explanatory
variables describing fire severity. The Albanita-Hooker Fire was the only fire where fuels
(vegetation type) were a factor in predicting fire severity. Pine, pinyon-juniper,
shrublands, and woodlands predicted higher fire severity than fir, grass, riparian and
sage brush. These findings are slightly different than found by Collins et al. (2007)
where white fir, lodgepole pine, and shrublands resulted in higher severity and red fir,
juniper, Jeffrey pine and meadows resulted in lower fire severity. However, it must be
noted that our vegetation grouping combine lodgepole pine and Jeffrey pine into one
pine group and red fir and white fir into one fire group. The fact that pine predicted
higher fire severity was interesting because Jeffrey pine historically burned frequently
with low intensity surface fires (van Wagtendonk and Fites-Kaufman 2006). The long
fire free interval (>100 yrs) found in the Albanita-Hooker fire might explain this deviation
from the expected. However, it might be advantageous to re-run the statistics dividing
out by pine species as was completed by Collins et al. (2007).
Relative humidity, temperature, and energy release component were the most common
explanatory values for change in canopy cover for the three fires.
Page
15
One of the goals of WFU is creating a mosaic of burned and re-growing patches
eventually allowing fires to burn more naturally. This mosaic is being recreated in areas
where UFW has been utilized for many decades, such as Yosemite National Park.
Collins et al. (2009) presents an explanation of two potential outcomes from WFU fires
reburning previous fires in Yosemite National Park, extent constrained or low to
moderate severity, and non-extent constrained moderate to high severity. It is proposed
the dominant factors controlling these outcomes to be time since last fire and weather. If
a fire burned less than 9 years prior it resulted in an extent-constrained fire, and the
probability of reburn is more likely under more extreme fire behavior as calculated using
the burning index (Collins et al. 2009). Although not explicitly explored in the analysis
both outcomes are evident in the Clover Fire (Figures 2 and 4). The Clover Fire
reburned the entire extent of two unnamed fires which occurred in 1944 and 1950, and
the 1997 Tunawee Fire (all greater than 9 years prior). In addition, the Clover Fire
appears to have been extent constrained by the more recent Broder-Beck (2006) and
Crag Fires (2004 and 2005). The one anomaly is the 1980 Clover Fire where a large
portion of that fire was reburned; however, there does appear to be some extent
constraints occurring. Without further analysis is it difficult to determine the exact
rational for this. Other factors which can limit the spread of a fire are natural barriers,
such as wet meadows and rock escarpments which are both present in the study are
and suppression actions. Even though these fires were managed as UFW, that does
not mean fire was permitted to freely burn at all times, suppression tactics are often
utilized to “heard” UFW fire to maintain desired fire behavior. In time, if fires are allowed
to burn as WFU fires, it might be possible for the Kern Plateau to return to a more
historic fire regime with a mosaic of fire resilient stands.
Wildland fire use can be used as a tool to reintroduce fire to the ecosystem, reduce
unnatural fuel accumulations, and promote resilient forest structures under appropriate
conditions. Weather and topographic features explained the majority of the variation in
burn severity. Coupling the knowledge of driving factors for fire severity and the
historical fire regime, future fires can be managed in such a way to promote desired fire
behavior and effects. For example, if high temperatures with low relative humidity are
expected and low severity is desired suppression tactics can be used to keep the fire in
check until more favorable burning conditions return.
Acknowledgements
This report would not have been possible without help from many people: Brent Skaggs,
SQF for coordinating efforts between SQF personnel and AMSET; Jay Miller, FAMSAC
and Jess Clark, RSAC for creating the RdNBR data; Jim Yearwood, SQF, Cherie Klein,
SQF, Scott Williams, SQF, Heidi Hosler, SQF, and Charlie Martin, BLM for supplying
GIS data and fire progression maps; Chris Clervi, AMSET for digitizing the fire
progression map for the Crag fire; and Scott Dailey, AMSET for editing and supplying
comments which have improved this report.
Page
16
References
Anderson, MK 2006. The use of fire by Native Americans in California. Pages 417-430
in: NG Sugihara, JW van Wagtendonk, J Fites-Kaufman, KE Shaffer, and AE Thode,
editors. Fire in California’s ecosystems. University of California Press, Berkeley,
California, USA.
Black, A. 2004. The West is Burning. What’s the Problem? Joint Fire Science Program,
Missoula, MT
Cleland, DT, JA Freeouf, JE Keys, GJ Nowacki, C Carpenter, and WH McNab 2007.
Ecological subregions: sections and subsections of the conterminous United States
[1:3,500,000] [CD-ROM]. Sloan AM, cartog. USDA Forest Service General Technical
Report WO-76
Collins, BM, and SL Stephens 2007. Managing Natural Fires in Sierra Nevada
Wilderness Areas. Frontiers in Ecology and the Environment 5(10): 523-52
Collins, BM, M Kelly, JW van Wagtendonk, and SL Stephens 2007. Spatial patterns of
large natural fires in Sierra Nevada wilderness areas. Landscape Ecology 22: 545557
Collins, BM, JD Miller, AE Thode, M Kelly, JW van Wagtendonk, and SL Stephens
2009. Interactions among wildland fires in a long-established Sierra Nevada natural
fire area. Ecosystems 12(1): 114-128
De’ath, G, and KE Fabricus 2000. Classification and regression trees: a powerful yet
simple technique for ecological data analysis. Ecology 81(11): 3178-3192
Doane, DL. J O'Laughlin, P Morgan, and C Miller 2006. Barriers to wildland fire use: A
preliminary problem analysis. International Journal of Wilderness. 12(1): 36-38
Finney, MA, CW McHugh, and IC Grenfell 2005. Stand and landscape effects of
prescribed burning on two Arizona wildfires. Canadian Journal of Forest Resources
35: 1714-1722
Graham, RT, S McCaffrey, and TB Jain 2004. Science basis for changing forest
structure to modify wildfire behavior and severity. USDA Forest Service, Rocky
Mountain Research Station, General Technical Report, RMRS-GTR-120
Keeley, JE 2009. Fire intensity, fire severity and burn severity: a brief review and
suggested usage. International Journal of Wildland Fire 18: 116-126
McGarigal, K, SA Cushman, MC Neel, and E Ene. 2002. FRAGSTATS: Spatial pattern
analysis program for categorical maps. Computer software program produced by the
authors at the University of Massachusetts, Amherst. Program available for
download at:: www.umass.edu/landeco/research/fragstats/fragstats
McKelvey, KS, and KL Busse 1996. Twentieth century fire patterns on Forest Service
lands. In: Sierra Nevada Ecosystem Project: Final Report to Congress Volume II,
Chapter 41. University of California Davis, Wildland Resource Center, Rep. 37
Miller, C 2003. Natural fire regimes in wilderness. International Journal of Wilderness
9(2): 33, 48.
Miller, C 2007. Simulation of the consequences of different fire regimes to support
wildland fire use decisions. Fire Ecology 3(2): 83-102
Miller, JD, and A Thode 2007. Quantifying burn severity in a heterogeneous landscape
with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing
of Environment 109(1):66-80
Page
17
Miller, JD, HD Safford, M Crimmins, and A Thode 2009a. Quantitative evidence for
increasing forest fire severity in the Sierra Nevada and southern Cascade
mountains, California and Nevada, USA. Ecosystems 12(1): 16-32
Miller, JD, EE Knapp, CH Key, CN Skinner, CJ Isbell, RM Creasy, JW Sherlock 2009b.
Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR)
to three measures of fire severity in the Sierra Nevada and Klamath Mountains,
California, USA. Remote Sensing of Environment 113(3): 645-656
Odion, DC, EJ Frost, JR Strittholt, H Jiang, DA Delasala, and MA Moritz 2004. Patterns
of Fire Severity and Forest Conditions in the Western Klamath Mountains, California.
Conservation Biology 18(4): 927-936
Pyne, SJ 1982. Fire in America: a cultural history of wildland and rural fire. Princeton
University Press, Princeton, New Jersey, USA
R Development Core Team 2008. R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria. Program
available for download at: http://www.R-project.org
RMRS [Rocky Mountain Research Station]/SEM [System for Environmental
Management] 2002. Fire Family Plus User’s Guide. Program available for download
at: www.fire.org
USDA [US Department of Agriculture Forest Service]/USDI [US Department of Interior
Park Service] 2005. Wildland Fire Use Implementation Procedures Reference
Guide. Washington, DC
USDA [US Department of Agriculture Forest Service]/USDI [US Department of Interior
Park Service] 2009. Guidance for Implementation of Federal Wildland Fire
Management Policy. Version: February 13, 2009 Available at:
http://www.nifc.gov/policies.htm
van Wagtendonk, JW 2007. The History and Evolution of Wildland Fire Use. Fire
Ecology 3(2): 3-17
van Wagtendonk, JW, and J Fites-Kaufman 2006. Sierra Nevada bioregion. Pages 264294 in: NG Sugihara, JW van Wagtendonk, J Fites-Kaufman, KE Shaffer, and AE
Thode, editors. Fire in California’s ecosystems. University of California Press,
Berkeley, California, USA.
Wells, G 2009. Wildland fire use: managing for a fire-smart landscape. Fire Science
Digest, a product of the Joint Fire Science Program. Issue 4, 11p.
Zimmerman, GT, and R Lasko 2006. The changing face of wildland fire use. Fire
Management Today 66(4): 7-12
Page
18